{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-09-11T03:13:24.984012Z",
     "start_time": "2025-09-11T03:13:21.865912Z"
    }
   },
   "source": [
    "import torch\n",
    "\n",
    "import pandas as pd\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "\n",
    "from d2l_learn.utils import *\n",
    "\n",
    "test_data = pd.read_csv(\"../dataset/house-prices-advanced-regression-techniques/test.csv\")\n",
    "train_data = pd.read_csv(\"../dataset/house-prices-advanced-regression-techniques/train.csv\")\n",
    "test_labels_data = pd.read_csv(\"../dataset/house-prices-advanced-regression-techniques/sample_submission.csv\")\n",
    "\n",
    "train_data.shape, test_data.shape"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1460, 81), (1459, 80))"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-11T03:13:25.032197Z",
     "start_time": "2025-09-11T03:13:25.019240Z"
    }
   },
   "cell_type": "code",
   "source": "train_data.iloc[0:4, [0, 1, 2, 3, -3, -2, -1]]",
   "id": "a07a96576befb3",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage SaleType SaleCondition  SalePrice\n",
       "0   1          60       RL         65.0       WD        Normal     208500\n",
       "1   2          20       RL         80.0       WD        Normal     181500\n",
       "2   3          60       RL         68.0       WD        Normal     223500\n",
       "3   4          70       RL         60.0       WD       Abnorml     140000"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-11T03:13:25.111338Z",
     "start_time": "2025-09-11T03:13:25.096853Z"
    }
   },
   "cell_type": "code",
   "source": "all_features = pd.concat((train_data.iloc[:, 1:-1], test_data.iloc[:, 1:]))",
   "id": "3abc51297489152b",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-11T03:13:25.250459Z",
     "start_time": "2025-09-11T03:13:25.207235Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 数据预处理：首先将所有的数值数据归一化\n",
    "numerical_features = all_features.dtypes[all_features.dtypes != \"object\"].index\n",
    "all_features[numerical_features] = all_features[numerical_features].apply(\n",
    "    lambda x: (x - x.mean()) / (x.std())\n",
    ")\n",
    "all_features[numerical_features] = all_features[numerical_features].fillna(0)"
   ],
   "id": "fa0384180fe3fc89",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-11T03:13:25.327751Z",
     "start_time": "2025-09-11T03:13:25.294573Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 为离散型变量创建独热编码\n",
    "all_features = pd.get_dummies(all_features, dummy_na=True)\n",
    "all_features.shape"
   ],
   "id": "651de49eec1b2e06",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 330)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-11T03:13:25.375205Z",
     "start_time": "2025-09-11T03:13:25.357353Z"
    }
   },
   "cell_type": "code",
   "source": [
    "n_train = train_data.shape[0]\n",
    "n_test = test_data.shape[0]\n",
    "\n",
    "# Convert all features to float32 to ensure compatibility with PyTorch\n",
    "# 当数据列中既有数值型数据又有 bool 型数据时，无法统一将 numpy转换为 torch，应该先用 astype 函数统一类型后进行转换\n",
    "X_train = torch.tensor(all_features[:n_train].astype(float).values, dtype=torch.float32)\n",
    "X_test = torch.tensor(all_features[n_train:].astype(float).values, dtype=torch.float32)\n",
    "Y_train = torch.tensor(train_data.iloc[:, -1].values.reshape(-1, 1), dtype=torch.float32)\n",
    "Y_test = torch.tensor(test_labels_data.SalePrice.values.reshape(-1, 1), dtype=torch.float32)\n",
    "X_train[:4], Y_train[:4]"
   ],
   "id": "2ffa3f14f6d2cc67",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[ 0.0673, -0.1844, -0.2178,  ...,  1.0000,  0.0000,  0.0000],\n",
       "         [-0.8735,  0.4581, -0.0720,  ...,  1.0000,  0.0000,  0.0000],\n",
       "         [ 0.0673, -0.0559,  0.1372,  ...,  1.0000,  0.0000,  0.0000],\n",
       "         [ 0.3025, -0.3986, -0.0784,  ...,  0.0000,  0.0000,  0.0000]]),\n",
       " tensor([[208500.],\n",
       "         [181500.],\n",
       "         [223500.],\n",
       "         [140000.]]))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-11T03:13:29.160868Z",
     "start_time": "2025-09-11T03:13:25.394311Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from torch import nn\n",
    "device = torch.device(\"mps\" if torch.backends.mps.is_available() else \"cpu\")\n",
    "num_inputs, num_outputs, num_hidden1, num_hidden2 = X_train.shape[1], 1, 256, 128\n",
    "\n",
    "# 定义模型\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(num_inputs, num_outputs)\n",
    "            # nn.Linear(num_inputs, num_hidden1),\n",
    "            # nn.ReLU(),\n",
    "            # nn.Linear(num_hidden1, num_hidden2),\n",
    "            # nn.ReLU(),\n",
    "            # nn.Linear(num_hidden2, num_outputs),\n",
    "        )\n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "net = Net().to(device)\n",
    "\n",
    "num_epochs, lr, wd, batch_size = 100, 5, 0, 64\n",
    "loss = nn.MSELoss().to(device)\n",
    "# 房价预测中关注的不应该是绝对误差，而是相对误差，并且由于价格本身数值较大，可以用对数的方式展示loss\n",
    "def log_rmse(net, features, target):\n",
    "    features = features.to(device)\n",
    "    target = target.to(device)\n",
    "    predictions = net(features)\n",
    "    clipped_preds = torch.clamp(predictions, 1, float(\"inf\"))\n",
    "    criterion = torch.sqrt(loss(torch.log(clipped_preds), torch.log(target)))\n",
    "    return criterion.item()\n",
    "\n",
    "writer = SummaryWriter(\"./logs\")\n",
    "\n",
    "def train(net, train_features, train_labels, test_features, test_labels, loss, epochs, lr, wd, batch_size):\n",
    "    train_loss, test_loss = [], []\n",
    "    train_loader = load_array((train_features, train_labels), batch_size)\n",
    "    optimizer = torch.optim.Adam(net.parameters(), lr=lr, weight_decay=wd)\n",
    "\n",
    "    step = 0\n",
    "    for epoch in range(epochs):\n",
    "        for X, y in train_loader:\n",
    "            X, y = X.to(device), y.to(device)\n",
    "            l = loss(net(X), y)\n",
    "            optimizer.zero_grad()\n",
    "            l.backward()\n",
    "            optimizer.step()\n",
    "            train_log_loss = log_rmse(net, X, y)\n",
    "            step += 1\n",
    "            writer.add_scalar(\"train_loss\", train_log_loss, step)\n",
    "\n",
    "        train_loss.append(log_rmse(net, train_features, train_labels))\n",
    "        if test_labels is not None:\n",
    "            test_log_loss = log_rmse(net, test_features, test_labels)\n",
    "            test_loss.append(test_log_loss)\n",
    "            writer.add_scalar(\"test_loss\", test_log_loss, epoch)\n",
    "    return train_loss, test_loss\n",
    "\n",
    "\n",
    "train(net, X_train, Y_train, X_test, Y_test, loss, num_epochs, lr, wd, batch_size)"
   ],
   "id": "293e569d3bcc3558",
   "outputs": [
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     "execution_count": 7,
     "metadata": {},
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   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-11T03:13:29.224037Z",
     "start_time": "2025-09-11T03:13:29.220778Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# k折交叉验证法 用于模型超参数选择\n",
    "def get_k_data(k, i, train_features, train_labels):\n",
    "    assert k > 1\n",
    "    fold_size = train_features.shape[0] // k\n",
    "    x_train, y_train, x_valid, y_valid = None, None, None, None\n",
    "    for j in range(k):\n",
    "        idx = slice(j * fold_size, (j + 1) * fold_size)\n",
    "        x_part, y_part = train_features[idx, :], train_labels[idx]\n",
    "        if j == i:\n",
    "            x_valid, y_valid = x_part, y_part\n",
    "        elif x_train is None and y_train is None:\n",
    "            x_train, y_train = x_part, y_part\n",
    "        else:\n",
    "            x_train = torch.cat([x_train, x_part], dim=0)\n",
    "            y_train = torch.cat([y_train, y_part], dim=0)\n",
    "    return x_train, y_train, x_valid, y_valid\n"
   ],
   "id": "9013450977d5d1b0",
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-11T03:13:29.310447Z",
     "start_time": "2025-09-11T03:13:29.306703Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def k_fold(k, train_features, train_labels, loss, epochs, lr, wd, batch_size):\n",
    "    train_l_sum, valid_l_sum = 0, 0\n",
    "    for i in range(k):\n",
    "        data = get_k_data(k, i, train_features, train_labels)\n",
    "        train_ls, valid_ls = train(net, *data, loss, epochs, lr, wd, batch_size)\n",
    "        train_l_sum += train_ls[-1]\n",
    "        valid_l_sum += valid_ls[-1]\n",
    "        if i == 2:\n",
    "           d2l.plot(list(range(1, num_epochs + 1)), [train_ls, valid_ls], xlabel='epoch', ylabel='rmse', xlim=[1, num_epochs], legend=['train', 'valid'], yscale='log')\n",
    "        print(f'折{i + 1}，训练log rmse{train_ls[-1]:f}, 'f'验证log rmse{valid_ls[-1]:f}')\n",
    "\n",
    "    return train_l_sum / k, valid_l_sum / k\n"
   ],
   "id": "a0697e029206156a",
   "outputs": [],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-11T03:13:42.962864Z",
     "start_time": "2025-09-11T03:13:29.336184Z"
    }
   },
   "cell_type": "code",
   "source": [
    "k = 5\n",
    "train_l, valid_l = k_fold(k, X_train, Y_train, loss, num_epochs, lr, wd, batch_size)\n",
    "print(f\"{k}折交叉验证，平均训练 log rmse:{float(train_l):f}, 平均验证 log rmse:{float(valid_l):f}\")"
   ],
   "id": "629ccab5eeb3f64d",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "折1，训练log rmse0.133662, 验证log rmse0.139873\n",
      "折2，训练log rmse0.125639, 验证log rmse0.140341\n",
      "折3，训练log rmse0.122439, 验证log rmse0.136883\n",
      "折4，训练log rmse0.126870, 验证log rmse0.123633\n",
      "折5，训练log rmse0.118905, 验证log rmse0.143194\n",
      "5折交叉验证，平均训练 log rmse:0.125503, 平均验证 log rmse:0.136785\n"
     ]
    },
    {
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     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
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    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-11T03:13:43.150838Z",
     "start_time": "2025-09-11T03:13:43.146398Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "def train_and_pred(train_features, train_labels, test_features, test_labels, loss, epochs, lr, wd, batch_size):\n",
    "    test_features = test_features.to(device)\n",
    "    train_features = train_features.to(device)\n",
    "    train_labels = train_labels.to(device)\n",
    "    test_labels = test_labels.to(device)\n",
    "    train_loss, test_loss = train(net, train_features, train_labels, test_features, test_labels, loss, epochs, lr, wd, batch_size)\n",
    "    d2l.plot(np.arange(1, num_epochs + 1), [train_loss, test_loss], xlabel = 'epochs', ylabel='rmse', xlim=[1, num_epochs], legend=['train', 'test'], yscale='log')\n",
    "    print(f'训练 log rmse:{float(train_loss[-1]):f}, 测试 log rmse：{float(test_loss[-1]):f}')\n",
    "    preds = net(test_features).to('cpu').detach().numpy()\n",
    "    test_data['SalePrice'] = pd.Series(preds.reshape(1, -1)[0])\n",
    "    submission = pd.concat([test_data['Id'], test_data['SalePrice']], axis=1)\n",
    "    submission.to_csv(f'../dataset/house-prices-advanced-regression-techniques/submission.csv', index=False)"
   ],
   "id": "ad2752c1c639e6d3",
   "outputs": [],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-11T03:13:50.018442Z",
     "start_time": "2025-09-11T03:13:43.442870Z"
    }
   },
   "cell_type": "code",
   "source": "train_and_pred(X_train, Y_train, X_test, Y_test, loss, num_epochs, lr, wd, batch_size)",
   "id": "5ef04ad978516d7b",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练 log rmse:0.122249, 测试 log rmse：0.421712\n"
     ]
    },
    {
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